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Product Tagging: Benefits, Challenges & Solutions in 2024

The global pandemic created a surge in online shopping, pushing e-commerce companies to improve their digital presence.

However, improving your digital presence is difficult, especially for companies that have wide and diverse product lines, such as Amazon, offering more than 12M products. 

The main challenge in online retail, for instance, is cart abandonment, which is about  70% of  So a way to address it is to ensure an easy shopping experience for the shoppers. Accurate product tagging is an effective tool in that regard. 

This article explores product tagging, why it is important for online businesses, what some challenges are in performing product tagging and how outsourcing/crowdsourcing and AI can help overcome them.

What is product tagging?

Product tagging is the process of adding data to products. This includes creating, assigning, and managing tags/labels for each product in order to describe and categorize it (see Figure 1).  

Product tagging  also helps in structuring the product within your inventory by categorizing it by its: 

Figure 1. Product tags for Men’s sports shoes

image of product tags on amazon
Source: Amazon

Why is product tagging important for E-commerce?

Product tagging is important due to the following reasons:

Makes the product discoverable

Product tags are what make the product identifiable on the internet by making it discoverable to both internal and external search engines.

When a keyword is searched on a website’s search function, the internal search engine matches those keywords to the product tags to show relevant results. Product tags also help categorize the products on the navigation bar and enable filters.

a screenshot of amazon's website with highlights on the search bar, filtering bar, and product results.
Source: Amazon

Improves customer experience

Product tags improve the customer’s shopping experience on the website because it makes it easier to search for the specific item on one’s mind and ensures the delivery of the most accurate result. 

This raises the customer’s satisfaction level and can ultimately increase sales because customers will find what they are looking for. 

Reduces product returns

A customer who is knowledgeable about what he/she is buying is less likely to return it because they know what to expect upon delivery. 

Products with up-to-date tags will provide accurate information to the customer, which will result in fewer returns, less customer troubleshooting, more positive experiences, and increasing numbers of loyal customers, all of which contribute to higher revenues.

Facilitates product recommendation

Detailed product tagging enables more personalized recommendations of products since it helps identify the attributes of the products that customers are more inclined towards, such as a “running shoe” with specific characteristics in terms of brand, model, color, and so forth (see Figure 2):

Recommended categories according to the searched keywords ‘Nike shoes’:

recommended categories from an online store after searching the keywords Nike shoes
Source: Trendyol

What are the challenges of product tagging?

The 3 major challenges in product tagging are:

1. Labor intensive & error-prone

Manual product tagging is a time-consuming and labor-intensive job. For online retailers and brands with hundreds, if not thousands of products, it can be very difficult to perform it in-house. 

If a small number of workers are assigned with the tagging of a large product line, errors and mistakes should be expected.

2. Extra costs

Hiring additional staff to overcome the previous challenge can add additional costs to the company’s budget. And online businesses, such as fast fashion companies that rely on seasonal products, cannot afford to hesitate to have their products ready in time. 

However, since the product line is ever increasing and changing, hiring extra workers to perform tagging will never be a forever fix; you will always need more workers. 

3. Difficulty in tagging

Tagging itself is not an easy task when a 1000-item product line is in question. You need to make sure that all the products’ tags are in sync with all the attributes and match with synonym keywords. This can become difficult when done manually by a small team, especially if the tagging is required in different languages.

How can Outsourcing/crowdsourcing help with product tagging?

To overcome the aforementioned challenges, outsourcing and crowdsourcing can be effective ways. If the company can not afford to hire a large team to perform product tagging, they can outsource/crowdsource it. However, before working with a third-party service provider, it is important to clarify your requirements and desired quality standards or make sure that the vendor offers flexibility.

How can AI help with Product tagging?

Another effective way of overcoming product tagging challenges is to automate the process through artificial intelligence (AI). More specifically, automated product tagging is done through visual-AI or computer vision (CV) to scan the images and tag them according to their visual attributes. 

This is how it works:

a flow chart with illustrations of how an automated product tagging system works


  • The automated system offers more accuracy since it accurately matches the attributes of the product to tags with multiple synonyms. This enables more accurate results for the keywords the shoppers are searching for.
  • The more accurate tags fuel better product recommendations
  • The software allows more efficiency and consistency since it does not tire. The time saved from manual tagging can be put elsewhere as is achieved by automating any other tedious business task.


  • AI-enabled solutions can be expensive to purchase and integrate into your existing system; therefore, it is important to consider the costs before investing.
  • Automated tagging software has limitations. Sometimes it can be difficult to detect hidden attributes/features from a product image. For instance, two models of a robotic vacuum cleaner can look the same but have different features which can not be simply detected from an image:
Images of 2 different models of a robotic vacuum cleaner looking almost identical.
Source: Amazon

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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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